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From Phenotypes to Molecules: Revolutionizing Gut Microbiota Identification Methods
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作者 WANG Xuan LV Chang-Long ZHAI Jing-Bo 《中国生物化学与分子生物学报》 CAS CSCD 北大核心 2024年第8期1065-1077,共13页
The gut microbiota is a complex ecosystem composed of many bacteria and their metabolites.It plays an irreplaceable role in human digestion,nutrient absorption,energy supply,fat metabolism,immune regulation,and many o... The gut microbiota is a complex ecosystem composed of many bacteria and their metabolites.It plays an irreplaceable role in human digestion,nutrient absorption,energy supply,fat metabolism,immune regulation,and many other aspects.Exploring the structure and function of the gut microbiota,as well as their key genes and metabolites,will enable the early diagnosis and auxiliary diagnosis of diseases,new treatment methods,better effects of drug treatments,and better guidance in the use of antibiotics.The identification of gut microbiota plays an important role in clinical diagnosis and treatment,as well as in drug research and development.Therefore,it is necessary to conduct a comprehensive review of this rapidly evolving topic.Traditional identification methods cannot comprehensively capture the diversity of gut microbiota.Currently,with the rapid development of molecular biology,the classification and identification methods for gut microbiota have evolved from the initial phenotypic and chemical identification to identification at the molecular level.This review integrates the main methods of gut microbiota identification and evaluates their application.We pay special attention to the research progress on molecular biological methods and focus on the application of high-throughput sequencing technology in the identification of gut microbiota.This revolutionary method for intestinal flora identification heralds a new chapter in our understanding of the microbial world. 展开更多
关键词 gut microbiota 16S rRNA real-time fluorescent qPCR high-throughput sequencing mass spectrum
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基于WorldView-2影像的土壤含盐量反演模型 被引量:8
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作者 吾木提.艾山江 买买提.沙吾提 +2 位作者 依力亚斯江.努尔麦麦提 茹克亚.萨吾提 王敬哲 《农业工程学报》 EI CAS CSCD 北大核心 2017年第24期200-206,共7页
针对WorldView-2影像高空间分辨率评价其定量反演土壤含盐量的能力,以盐渍化现象较为明显的新疆克里雅河流域为研究对象,基于WorldView-2影像和实测高光谱数据,利用偏最小二乘回归(partial least squares regression,PLSR)和BP人工神经... 针对WorldView-2影像高空间分辨率评价其定量反演土壤含盐量的能力,以盐渍化现象较为明显的新疆克里雅河流域为研究对象,基于WorldView-2影像和实测高光谱数据,利用偏最小二乘回归(partial least squares regression,PLSR)和BP人工神经网络(back propagation artificial neural networks,BP ANN)方法建立定量反演该流域土壤含盐量模型并做出研究区高空间分辨率土壤含盐量分布图。结果表明:1)利用实测高光谱数据和影像数据分别建立的2种模型中BP神经网络模型预测精度都高于PLSR模型,其中基于影像数据建立的6:8:1结构的3层BP神经网络模型决定系数R2、均方根误差RMSE、相对分析误差RPD分别为0.851、0.979、2.337,模型的稳定性和预测能力都优于PLSR模型(R2、RMSE、RPD分别为0.814、1.139、2.007)。2)利用WorldView-2影像提高了土壤含盐量制图的空间分辨率,归一化植被指数NDVI和比例植被指数RVI较有效降低了植被覆盖与土壤水分对预测精度的影响。该文建立的考虑植被覆盖与土壤水分定量反演土壤含盐量的模型不需要复杂的参数,一定程度上满足了干旱、半干旱地区的盐渍化监测需求,可以促进WorldView-2等高空间分辨率卫星在盐渍化监测中的进一步应用。 展开更多
关键词 遥感 土壤 盐分测量 WorldView-2影像 克里雅河流域 实测高光谱 神经网络 反演模型
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